Scientific Assistant for Biomedical Data Science

The Biomedical Data Science (BMDS) Lab investigates data-driven solutions for healthcare applications with a focus on neurological conditions such as spinal cord injury (SCI), lower back pain, neuro-degenerative disorders and neurological tumors. At the core of our research is the collaboration across disciplines spanning expertise in medicine, biology, computer and data science. We are seeking a scientific assistant to join this growing team and contribute to interdisciplinary research partnerships. The anticipated start date is July 1, 2025.

Traumatic SCI has profound and lifelong implications for affected patients and their families. A major challenge in drug development for traumatic SCI is the high failure rate of clinical trials despite promising preclinical evidence. One of the key obstacles is the assumption that SCI can be treated without accounting for substantial variability in individual biological, clinical, health, and injury-related characteristics. Traditionally, a uniform physiological recovery capacity and consistent benefits from experimental treatments are assumed, contradicting the established understanding of natural recovery variability. The proposed project aims to bridge this critical gap by distinguishing between patients’ baseline natural recovery and treatment-induced improvements. By isolating the true treatment effect, the project seeks to provide a clearer and more accurate assessment of how interventions influence recovery trajectories.

The scientific assistant will:

• Familiarize themselves with the various international databases collecting data on SCI patients. These data are readily available at the start of this project.
• Implement, train, and benchmark state-of-the-art data science pipelines to characterize SCI recovery trajectories and injury patterns. Integrate personalized physiological measurements into a recovery prediction model, while adapting Bayesian Neural Networks for SCI data and analyzing the impact on model uncertainty.
• Develop and implement methods to utilize predicted natural recovery to effectively isolate, analyze, and quantify treatment-induced improvements in SCI patients.
• Develop an exploratory web platform for traumatic SCI recovery. Collaborate with clinical experts to gather and define requirements, ensuring the platform aligns with the needs of both patients and healthcare professionals.

• You hold a Master's degree in a relevant field such as data science, computer science, physics, computational biology, or biomedical research.
• You are proficient in Python programming, with experience in statistical analysis and implementing machine and deep learning models using Keras/TensorFlow and/or PyTorch.
• You have experience in collaborative coding, version control, and utilizing computer clusters.
• Ideally, you have a background in biomedical projects and experience in interdisciplinary collaboration. 
• Experience in SCI-related research is a plus.
• You are motivated to work as part of a diverse team and are committed to scientific excellence in your field.
• You are proficient in both written and spoken English.

We offer a 1-year project-based contract at the BMDS Lab (80–100% workload), with the potential for a second-year extension. The position includes:

• Opportunities to engage with diverse communities bridging data science and SCI research, leading to high-impact publications.
• Enhance your data science skills while gaining insights into the biomedical aspects of critical health conditions, with a focus on SCI.
• Be part of a highly motivated, multidisciplinary, and collaborative team.
• Learn from experts in the field and contribute to an active research lab.
• We will support your scientific career development and application for doctoral fellowships, if desired.